Bastian Rieck Pseudomanifold

## fix_bibliography_links_springer.js
// ==UserScript==
// @name     Fix bibliography links @ Springer
// @author   Pseudomanifold
// @include  https://link.springer.com/*
// @include  https://*.springeropen.com/*
// @include  https://*.nature.com/*
// @require  http://ajax.googleapis.com/ajax/libs/jquery/3.7.1/jquery.min.js
// @version  0.0.2
// ==/UserScript==

## neurips_2021.sty
% partial rewrite of the LaTeX2e package for submissions to the
% Conference on Neural Information Processing Systems (NeurIPS):
%
% - uses more LaTeX conventions
% - line numbers at submission time replaced with aligned numbers from
%   lineno package
% - \nipsfinalcopy replaced with [final] package option
% - automatically loads times package for authors
% - loads natbib automatically; this can be suppressed with the
%   [nonatbib] package option

## create_hdf5_test_file.py
#!/usr/bin/env python3
#
# Creates a simple 3x3 HDF5 test file. The file will contain
# a single group and a single *simple* data set. The numbers
# from 1 to 9 will be distributed over the data.

import h5py

f = h5py.File("Simple.hdf5", "w")
d = f.create_dataset("Simple", (3,3), dtype='f')

## selection_without_replacement.cc
#include <iostream>
#include <limits>
#include <random>
#include <vector>

int main(int, char**)
{
  std::random_device r;
  std::default_random_engine e( r() );

## 3_peaks_ridge.vtk
# vtk DataFile Version 3.0
vtk output
ASCII
DATASET STRUCTURED_GRID
DIMENSIONS 50 50 2
POINTS 5000 float
0 0 0 0.020408 0 0 0.040816 0 0
0.061224 0 0 0.081633 0 0 0.102041 0 0
0.122449 0 0 0.142857 0 0 0.163265 0 0
0.183673 0 0 0.204082 0 0 0.22449 0 0

## embed_matrix_mds.py
#!/usr/bin/env python3
#
# Embeds a distance matrix into 2D using MDS. Distances are normalized
# to [0,1] in order to facilitate comparing embedded matrices.

import numpy
import sys

from sklearn.manifold import MDS

## iris_scatterplot_matrix.gnuplot
set size square

unset key
unset xtics
unset ytics

binwidth=0.1
bin(x,width)=width*floor(x/width) + binwidth/2.0

set boxwidth 0.9*binwidth

## git_count_words_latex.sh
#!/bin/bash
#
# This script traverses all commits of a git repository in the current directory
# and counts the number of words that are changed, i.e. added or deleted for all
# TeX files. The output contains a time-stamp (YYYY-MM-DD). Next, there are some
# counts, viz. the number of added, deleted, and total words.
#
# You can use `gnuplot`, for example, to create nice visualizations from the raw
# data. For this, it may be useful to specify the first column as a date column:
#

## keybase.md

      
        
          
            
              
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                Pseudomanifold
                / keybase.md
            
            
              Last active
              April 15, 2019 19:39
            
          
        
      
        
  
      
    Keybase proof

I hereby claim:

I am pseudomanifold on github.
I am brieck (https://keybase.io/brieck) on keybase.
I have a public key ASCGmhrlUo3IPOtFqrK-i4LL8r9ZIOAkBeLzMCQBgePv6Qo

To claim this, I am signing this object:
	// ==UserScript==
	// @name Fix bibliography links @ Springer
	// @author Pseudomanifold
	// @include https://link.springer.com/*
	// @include https://.springeropen.com/
	// @include https://.nature.com/
	// @require http://ajax.googleapis.com/ajax/libs/jquery/3.7.1/jquery.min.js
	// @version 0.0.2
	// ==/UserScript==
	% partial rewrite of the LaTeX2e package for submissions to the
	% Conference on Neural Information Processing Systems (NeurIPS):
	%
	% - uses more LaTeX conventions
	% - line numbers at submission time replaced with aligned numbers from
	% lineno package
	% - \nipsfinalcopy replaced with [final] package option
	% - automatically loads times package for authors
	% - loads natbib automatically; this can be suppressed with the
	% [nonatbib] package option
	#!/usr/bin/env python3
	#
	# Creates a simple 3x3 HDF5 test file. The file will contain
	# a single group and a single simple data set. The numbers
	# from 1 to 9 will be distributed over the data.

	import h5py

	f = h5py.File("Simple.hdf5", "w")
	d = f.create_dataset("Simple", (3,3), dtype='f')
	#include <iostream>
	#include <limits>
	#include <random>
	#include <vector>

	int main(int, char**)
	{
	std::random_device r;
	std::default_random_engine e( r() );
	# vtk DataFile Version 3.0
	vtk output
	ASCII
	DATASET STRUCTURED_GRID
	DIMENSIONS 50 50 2
	POINTS 5000 float
	0 0 0 0.020408 0 0 0.040816 0 0
	0.061224 0 0 0.081633 0 0 0.102041 0 0
	0.122449 0 0 0.142857 0 0 0.163265 0 0
	0.183673 0 0 0.204082 0 0 0.22449 0 0
	#!/usr/bin/env python3
	#
	# Embeds a distance matrix into 2D using MDS. Distances are normalized
	# to [0,1] in order to facilitate comparing embedded matrices.

	import numpy
	import sys

	from sklearn.manifold import MDS
	set size square

	unset key
	unset xtics
	unset ytics

	binwidth=0.1
	bin(x,width)=width*floor(x/width) + binwidth/2.0

	set boxwidth 0.9*binwidth
	#!/bin/bash
	#
	# This script traverses all commits of a git repository in the current directory
	# and counts the number of words that are changed, i.e. added or deleted for all
	# TeX files. The output contains a time-stamp (YYYY-MM-DD). Next, there are some
	# counts, viz. the number of added, deleted, and total words.
	#
	# You can use `gnuplot`, for example, to create nice visualizations from the raw
	# data. For this, it may be useful to specify the first column as a date column:
	#